Graphs and Hypergraphs
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
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Community structure is very important for many real-world networks. It has been shown that communities are overlapping and hierarchical. However, most previous methods, based on the graph model, can't investigate these two properties of community structure simultaneously. Moreover, in some cases the use of simple graphs does not provide a complete description of the real-world network. After introducing hyper graphs to describe real-world networks and defining hyper-edge similarity measurement, we propose a Hierarchical Clustering method based on Hyper-edge Similarity (HCHS) to simultaneously detect both the overlapping and hierarchical properties of complex community structure, as well as using the newly introduced community density to evaluate the goodness of a community. The examples of application to real-world networks give excellent results.